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import streamlit as st
from transformers import MarianMTModel, MarianTokenizer
# Define available languages with MarianMT models
LANGUAGES = {
'Spanish': 'es',
'French': 'fr',
'German': 'de',
'Chinese': 'zh',
'Hindi': 'hi',
'Arabic': 'ar',
'Japanese': 'ja',
'Russian': 'ru',
'Italian': 'it',
'Portuguese': 'pt',
# Add more languages if needed
}
# Function to load the model based on the selected language
@st.cache_resource
def load_model(src_lang='en', tgt_lang='es'):
model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}'
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)
return model, tokenizer
# Function to translate text
def translate_text(model, tokenizer, text):
inputs = tokenizer.encode(text, return_tensors='pt', truncation=True, padding=True)
translated = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
# Streamlit app
st.title("Language Translator")
st.write("Translate English text to any language using Hugging Face models.")
# Input text
text = st.text_area("Enter text in English to translate:")
# Language selection
language = st.selectbox("Choose target language", list(LANGUAGES.keys()))
if st.button("Translate"):
if text:
# Load model and tokenizer based on selected language
tgt_lang = LANGUAGES[language]
model, tokenizer = load_model('en', tgt_lang)
# Perform translation
translated_text = translate_text(model, tokenizer, text)
# Display the translation
st.write(f"**Translated text ({language}):**")
st.write(translated_text)
else:
st.write("Please enter text to translate.")
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